An FPGA-based Hardware Accelerator for Scene Text Character Recognition

Scene text character recognition is a challenging task in Computer Vision since natural scene images usually have cluttered background and the character's size, font, orientation, texture, brightness, and alignment in the picture are variable and non-predictable. Furthermore, most systems including scene text character recognition are usually embedded in a system on a chip (SoC), which has critical requirements, such as low latency, low area, mobility, and flexibility, at the same time that they require high accuracy. In this context, in this work we propose a heterogeneous system for embedded applications with time, area and power constraints, that combines hardware and software to accelerate a technique for scene text character recognition, based on Histogram of Oriented Gradients (HOG) for feature extraction and a neural network Extreme Learning Machine (ELM) as a classifier. The system was prototyped and experimented in the Terasic embedded platform DE2i-150 and the results showed that the system has accuracy of 65.5% in the Chars74k-15 dataset and is able to process up to 11 frames per second, having a good trade-off between processing time and accuracy in embedded environments. Moreover, it occupies only 11% logic elements of the Altera Cyclone IV FPGA, enabling its use in embedded systems.